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The distribution of self-reported psychotic-like experiences in non-psychotic help-seeking mental health patients in the general population: a factor mixture analysis

  • Judith Rietdijk
  • , Marjolein Fokkema
  • , Daniel Stahl
  • , Lucia Valmaggia
  • , Helga K. Ising
  • , Sara Dragt
  • , Rianne Klaassen
  • , Dorien Nieman
  • , Rachel Loewy
  • , Pim Cuijpers
  • , Philippe Delespaul
  • , Don Linszen
  • , Mark van der Gaag

    Research output: Journal contributionsJournal articlesResearchpeer-review

    18 Citations (Scopus)

    Abstract

    Purpose: Factor mixture analysis (FMA) and item response mixture models in the general population have shown that the psychosis phenotype has four classes. This study attempted to replicate this finding in help-seeking people accessing mental health services for symptoms of non-psychotic mental disorders. Methods: All patients (18–35 years old) referred for nonpsychotic mental health problems to the secondary mental healthcare service in The Hague between February 2008 to February 2010 (N = 3,694), were included. Patients completed the Prodromal Questionnaire (PQ). Hybrid latent class analysis was applied to explore the number, size and symptom profiles of the classes. Results: The FMA resulted in four classes. Class 1 (N = 1,039, 28.1 %) scored high on conceptual disorganization, inattention and mood disorder. Patients in Class 2 (N = 619, 16.8 %) endorsed almost all PQ-items, were more often screened as being psychotic or at high risk of developing psychosis, without care takers noticing. In Class 3 (N = 1,747, 47.3 %) perplexity, paranoia and negative symptoms were more prevalent. Patients were more often at high risk of developing psychosis. Class 4 (N = 286, 7.7 %) represented the ‘normative’ group with low probabilities for all items. Discussion: The results support the hypothesis that a representation in four classes of psychotic-like experiences can also be applied in a help-seeking population.
    Original languageEnglish
    JournalSocial Psychiatry and Psychiatric Epidemiology
    Volume49
    Issue number3
    Pages (from-to)349-358
    Number of pages10
    ISSN0933-7954
    DOIs
    Publication statusPublished - 03.2014

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

    Research areas and keywords

    • Health sciences
    • psychosis continuum
    • General help-seeking population
    • Prodromal symptoms
    • Psychotic-like experiences
    • Psychology

    ASJC Scopus Subject Areas

    • Epidemiology
    • Health(social science)
    • Psychiatry and Mental health
    • Social Psychology

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